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Exponential time differencing methods is a power tool for high-performance numerical simulation of computationally challenging problems in condensed matter physics, fluid dynamics, chemical and biological physics, where mathematical models…

Numerical Analysis · Mathematics 2024-10-15 Evelina V. Permyakova , Denis S. Goldobin

A new Chebyshev-type family of stabilized explicit methods for solving mildly stiff ODEs is presented. Besides conventional conditions of order and stability we impose an additional restriction on the methods: their stability function must…

Numerical Analysis · Mathematics 2025-04-02 Boris Faleichik , Andrew Moisa

When evolving in time the solution of a hyperbolic partial differential equation, it is often desirable to use high order strong stability preserving (SSP) time discretizations. These time discretizations preserve the monotonicity…

Numerical Analysis · Mathematics 2017-08-02 Sidafa Conde , Sigal Gottlieb , Zachary J. Grant , John N. Shadid

In this paper, we study symmetric integrators for solving second-order ordinary differential equations on the basis of the notion of continuous-stage Runge-Kutta-Nystrom methods. The construction of such methods heavily relies on the…

Numerical Analysis · Mathematics 2024-12-20 Wensheng Tang , Jingjing Zhang

In this paper a technique is given to recover the classical order of the method when explicit exponential Runge-Kutta methods integrate reaction-diffusion problems. Although methods of high stiff order for problems with vanishing boundary…

Numerical Analysis · Mathematics 2022-11-22 Begoña Cano , Marí a Jesús Moreta

This paper contains an error analysis of two randomized explicit Runge-Kutta schemes for ordinary differential equations (ODEs) with time-irregular coefficient functions. In particular, the methods are applicable to ODEs of Carath\'eodory…

Numerical Analysis · Mathematics 2017-07-13 Raphael Kruse , Yue Wu

Implicit methods for the numerical solution of initial-value problems may admit multiple solutions at any given time step. Accordingly, their nonlinear solvers may converge to any of these solutions. Below a critical timestep, exactly one…

Numerical Analysis · Mathematics 2019-12-20 K. R. Green , G. W. Patrick , R. J. Spiteri

This paper investigates the performance of a subclass of exponential integrators, specifically explicit exponential Runge--Kutta methods. It is well known that third-order methods can suffer from order reduction when applied to linearized…

Numerical Analysis · Mathematics 2024-12-30 Thi Tam Dang , Trung Hau Hoang

Recently a useful finite-difference scheme was proposed in [Phys. Rev. E 98, 033302 (2018)] to solve Fokker-Planck equations with drift-admitting jumps. However, while the scheme is fifth order for the case with smooth drifts, it is only…

Statistical Mechanics · Physics 2019-11-05 Yaming Chen , Xiaogang Deng

We develop continuous-stage Runge-Kutta-Nystr\"Om (csRKN) methods in this paper. By leading weight function into the formalism of csRKN methods and modifying the original pattern of continuous-stage methods, we establish a new and larger…

Numerical Analysis · Mathematics 2018-07-26 Wensheng Tang

In this work we present explicit Adams-type multistep methods with extended stability interval, which are analogous to the stabilized Chebyshev Runge--Kutta methods. It is proved that for any $k\geq 1$ there exists an explicit $k$-step…

Numerical Analysis · Mathematics 2020-12-15 Vasily Repnikov , Boris Faleichik , Andrey Moysa

A new class of third order Runge-Kutta methods for stochastic differential equations with additive noise is introduced. In contrast to Platen's method, which to the knowledge of the author has been up to now the only known third order…

Numerical Analysis · Mathematics 2010-09-29 Kristian Debrabant

Stabilized methods (also called Chebyshev methods) are explicit methods with extended stability domains along the negative real axis. These methods are intended for large mildly stiff problems, originating mainly from parabolic PDEs. In…

Numerical Analysis · Mathematics 2023-03-30 Andrew Moisa , Boris Faleichik

Runge-Kutta methods are a popular class of numerical methods for solving ordinary differential equations. Every Runge-Kutta method is characterized by two basic parameters: its order, which measures the accuracy of the solution it produces,…

Numerical Analysis · Mathematics 2019-11-04 David K. Zhang

Efficient high order numerical methods for evolving the solution of an ordinary differential equation are widely used. The popular Runge--Kutta methods, linear multi-step methods, and more broadly general linear methods, all have a global…

Numerical Analysis · Mathematics 2020-03-16 Adi Ditkowski , Sigal Gottlieb , Zachary J. Grant

We study Runge-Kutta methods for rough differential equations which can be used to calculate solutions to stochastic differential equations driven by processes that are rougher than a Brownian motion. We use a Taylor series representation…

Numerical Analysis · Mathematics 2020-03-31 Martin Redmann , Sebastian Riedel

The design of numerical integrators for solving stochastic dynamics with high weak order relies on tedious calculations and is subject to a high number of order conditions. The original approaches from the literature consider strong…

Numerical Analysis · Mathematics 2026-03-26 Adrien Busnot Laurent , Kristian Debrabant , Anne Kværnø

Finite element discretization of time dependent problems also require effective time-stepping schemes. While implicit Runge-Kutta methods provide favorable accuracy and stability problems, they give rise to large and complicated systems of…

Numerical Analysis · Mathematics 2023-05-01 Robert C. Kirby

We have shown previously that functionally fitted Runge-Kutta (FRK) methods can be studied using a convenient collocation framework. Here, we extend that framework to functionally fitted Runge-Kutta-Nystr\"om (FRKN) methods, shedding…

Numerical Analysis · Mathematics 2014-10-17 N. S. Hoang , R. B. Sidje

Different families of Runge-Kutta-Nystr\"om (RKN) symplectic splitting methods of order 8 are presented for second-order systems of ordinary differential equations and are tested on numerical examples. They show a better efficiency than…

Numerical Analysis · Mathematics 2022-07-26 F. Casas , S. Blanes , A. Escorihuela-Tomàs